PARENTAL MANAGEMENT, ADHD, AND DELINQUENT INVOLVEMENT:

REASSESSING GOTTFREDSON AND HIRSCHI'S GENERAL THEORY

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ABSTRACT

A core but underresearched proposition of Gottfredson and Hirschi’s (1990) general theory

of crime is that ineffective parenting fosters low self-control in children, which in turn leads

to involvement in delinquent conduct. Using a sample of 2,472 students from six middle

schools, we test this thesis. In the context of the general theory, we also examine the impact

of ADHD on self-control and delinquency. The analysis revealed three main conclusions.

First, consistent with the general theory, low self-control was a strong predictor of both self-

reported delinquency and self-reported arrests. Second, inconsistent with the general theory,

parental monitoring not only increased self-control but also had direct effects on both

measures of delinquency. Third, the effects of ADHD on delinquency were largely through

low self-control. This finding both illuminates a potentially important pathway from ADHD

to misconduct and suggests that, contrary to the general theory; low self-control may have

multiple determinants.

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PARENTAL MANAGEMENT, ADHD, AND DELINQUENT INVOLVEMENT:

REASSESSING GOTTFREDSON AND HIRSCHI'S GENERAL THEORY

Gottfredson and Hirschi’s general theory has emerged as a salient criminological

paradigm. It has received over 300 citations in the Social Science Citation Index and is

discussed in virtually every book on contemporary criminological theory (see, e.g., Akers,

2000; Cullen and Agnew, 1999; Lilly, Cullen, and Ball, 2002; Paternoster and Bachman,

2001; Vold, Bernard, and Snipes, 1998). Gottfredson and Hirschi’s general theory claims

that low self-control is the principal source of juvenile delinquency, adult crime, and

“analogous” behaviors. After more than a decade of research, the existing studies show

consistent empirical support for this proposition (Pratt and Cullen, 2000). These

investigations suggest that criminal and related behaviors are higher among adolescents and

adults who behave impulsively, enjoy taking risks, prefer physical activities to mental

exertion, are self-centered and short-tempered, and choose simple tasks over complex ones.

Gottfredson and Hirschi (1990) attribute low self-control to ineffective parenting and

contend that inculcating self-control requires parents to monitor their children so they can

effectively recognize and punish wayward acts. In essence, Gottfredson and Hirschi link

self-control to what other criminologists have called parental “direct control” (Wells and

Rankin, 1998) or “instrumental control” (Hagan, 1989). They disagree with current scholars

who argue that parents have little impact on young adolescents (Harris, 1998), but also assert

that when parental influence occurs, it is exerted through the narrow conduit of self-control.

Phrased alternatively, they dismiss the notion that parenting could affect delinquency directly

or through other social processes (e.g., social learning, social support).

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Despite its centrality to the general theory, relatively few studies have examined

Gottfredson and Hirschi’s thesis on parental management, control, and crime (for exceptions,

see Hay, 2001; Gibbs, Giever, and Martin, 1998; Feldman and Weinberger, 1994). In part,

this neglect is due to researchers using older samples (i.e., late teens or adults) and/or

samples that do not contain measures of parental management and self-control. A main goal

of this study is to help fill this void by providing a test of this core proposition of the general

theory that parental management affects crime through self-control.

Additionally, the criminological research has not explored whether self-control has

sources beyond parental management. Again, Gottfredson and Hirschi (1990:97) are clear in

their assertion that “ineffective child rearing” is the “major” cause of low self-control. Our

data set allows us to assess this proposition by investigating whether Attention Deficit

Hyperactivity Disorder (ADHD)—an individual difference with a strong genetic

component—might be an alternative source of delinquency and of low self-control. If so,

then Gottfredson and Hirschi’s analysis of the origins of low self-control might require

further specification.

In short, our study attempts to examine two core propositions of the general theory of

crime: first, whether low self-control is the sole cause of juvenile delinquency and, second,

whether ineffective parenting is the exclusive cause of low self-control. We begin by

examining the research on parenting as it relates to the general theory and then by reviewing

research on ADHD and misconduct. These sections are a prelude to our outlining the

research strategy for our empirical analysis.

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PARENTING AND THE GENERAL THEORY OF CRIME

The basic premise of Gottfredson and Hirschi’s general theory of crime is as follows.

People who lack self-control will tend to be impulsive, insensitive, physical (as

opposed to mental), risk-taking, shortsighted, and nonverbal, and they will tend

therefore to engage in criminal and analogous acts. Since these traits can be

identified prior to the age of responsibility for crime, since there is considerable

tendency for these traits to come together in the same people, and since the traits

tend to persist through life, it seems reasonable to consider them as comprising a

stable construct useful in the explanation of crime (Gottfredson and Hirschi,

1990:90-91).

Thus, Gottfredson and Hirschi argue that at a very early age, individuals fail to develop the

ability to control their behavior and therefore are prone to engage in risky behaviors that give

them either a short-term reward or relief from momentary irritations (i.e., criminal behavior).

Furthermore, they argue that once children develop this inability to exercise control, they will

not be able to control their impulses over their life span (i.e., they will offend throughout

their lives).

Support for an inverse relationship between self-control and deviant behavior is

consistent. Research has shown that low self-control is significantly related to general law

violations and to self-reported delinquency (Grasmick et al., 1993; Tremblay, Boulerice,

Arseneault, and Niscale, 1995). Additionally, Vazsonyi, Pickering, Junger, and Hessing

(2001) found that the Grasmick et al. (1993) self-control scale had very similar predictive

ability cross-nationally (United States, Switzerland, Hungary, and Netherlands). A recent

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meta-analysis also gives credence to the argument that low self-control is related to criminal

behavior. Pratt and Cullen’s (2000) meta-analysis included 21 empirical studies spanning

nearly a decade of research and 126 effect size estimates representing nearly 50,000

individual cases. They designed their meta-analysis to assess whether the extant research

supports Gottfredson and Hirschi’s theory. Pratt and Cullen found that whether it is in its

weighted, unweighted, or independence-adjusted form, self-control is a strong predictor of

crime. They report also that the effect size of the self-control variable is not significantly

affected by whether the scale used to measure it is Grasmick et al.’s (1993) or an alternative

one. In addition, Pratt and Cullen found that the size of the self-control variable remains

relatively constant regardless of the other variables included in the analysis, and that self-

control has “general” effects.

What, however, is the source of low self-control? Gottfredson and Hirschi state that

the major cause of low self-control is when parents (or guardians) fail to effectively raise

their children during the first eight years of their lives. Their definition of ineffective

parenting includes three components: (1) monitoring or tracking the child’s behavior, (2)

recognition of deviant behavior when it occurs, and (3) consistent and proportionate

punishment for the deviant behavior when it is recognized. Thus, Gottfredson and Hirschi

argue that during childhood, people develop low self-control if their parents fail to effectively

monitor their behavior and appropriately hold them accountable for their deviant behaviors.

Quite simply, crime could be prevented if parents effectively raised their children (p. 102).

Notably, Gottfredson and Hirschi clearly downplay the possibility that low self-

control has a genetic/biological component. For example, after a careful analysis of adoption

studies, they argue that this research provides “strong evidence that the inheritance of

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criminality is minimal….we conclude that the ‘genetic effect’…is near zero” (p. 60,

emphasis in the original). They also observe that, “obviously, we do not suggest that people

are born criminals, inherit a gene from criminality, or anything of that sort. In fact, we

explicitly deny such notions…” (p. 96). Gottfredson and Hirschi, however, raise the

possibility that “individual differences may have an impact on the prospects for effective

socialization (or adequate control)” (p. 96). But in contrast to other theorists who

systematically incorporate individual differences into their models (e.g., Moffitt, 1993;

Sampson and Laub, 1994; Wilson and Herrnstein, 1985), they fail to develop further the

interplay between individual differences and self-control. In fact, they assert, “effective

socialization is…always possible whatever the configuration of individual traits” (p. 96).

Further, in their most recent statement of their theory (Hirschi and Gottfredson, 2001), they

refrain from mentioning the idea that individual traits—such as genetic/biological

predispositions—could underlie self-control. Instead, they suggest that at the inception of

life, criminal propensity is virtually universal, for “all of us…are born with the ability to use

force and fraud in the pursuit of our private goals” (2001:90). In the end, Gottfredson and

Hirschi are clear in their contention that parenting is the key factor in differentiating whether

children do, or do not, develop the ability to resist the gratification inherent in criminal

conduct; or, as they put it, the “major ‘cause’ of low self-control thus appears to be

ineffective child-rearing” (1990:97).

Few criminological studies have tested the proposition that ineffective parenting

causes low self-control. Even so, those that have been conducted are largely supportive of

the general theory. Thus, Feldman and Weinberger (1994) report on a sample of sixth-grade

boys and their families who were followed up four years later (n = 81). They examined

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whether self-control mediates the link between ineffective parenting during preadolescence

and subsequent adolescent delinquency. Feldman and Weinberger’s measure of self-control

included a self-report rating by the boys using the Weinberger Adjustment Inventory and

ratings by their teachers, parents, and peers. Using self-reports, the boys assessed ineffective

parenting along multiple dimensions, including inconsistency, rejection versus child-

centeredness, and power assertive/harsh discipline. In addition, observers assessed general

family functioning. Feldman and Weinberger’s (1994) path analysis showed that ineffective

parenting was a significant predictor of low self-control, which, in turn, predicted delinquent

behavior. Additionally, they found that low self-control completely mediated the influence

of ineffective parenting on delinquency.

Gibbs et al. (1998) undertook a systematic analysis of the general theory’s parenting

thesis. Using retrospective accounts of family management practices collected from 262

university students, they reported that ineffective parenting had a significant indirect effect

on their deviance index through a 40-item self-control scale. Similar to Feldman and

Weinberger (1994), they found that parental management did not directly affect their

deviance index once they controlled for low self-control. Their finding supports Gottfredson

and Hirschi’s (1990) contention that low self-control should entirely mediate the influence of

ineffective parenting on criminal behavior.

Most recently, Hay (2001) analyzed the impact of parental monitoring and parental

willingness to discipline on self-control and “projected” delinquency (or intentions to offend)

among 197 urban high schools students in a southwestern state. Again, the results are largely

consistent with the general theory. Self-control was influenced in the predicted direction by

parental management practices and self-control directly affected delinquency. However,

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contrary to the research by Feldman and Weinberger (1994) and Gibbs et al. (1998), Hay

(2001) found that a scale measuring parental monitoring and discipline was associated with

both “predatory delinquency” and “substance use” after controlling for the effect of low self-

control.

ADHD, SELF-CONTROL, AND CRIME

ADHD is the most common neurobehavioral disorder of childhood and is among the

most prevalent chronic health conditions affecting school-aged children (Homer et al., 2000).

The core symptoms of ADHD include inattention, hyperactivity, and impulsivity. The

assessment and diagnosis of school-aged children with attention-deficit/hyperactivity

disorder (ADHD) include six recommended criteria:

1) in a child 6 to 12 years old who presents with inattention, hyperactivity,

impulsivity, academic underachievement, or behavior problems, primary care

clinicians should initiate an evaluation for ADHD; 2) the diagnosis of ADHD requires

that a child meet Diagnostic and Statistical Manual of Mental Disorders, Fourth

Edition criteria; 3) the assessment of ADHD requires evidence directly obtained from

parents or caregivers regarding the core symptoms of ADHD in various settings, the

age of onset, duration of symptoms, and degree of functional impairment; 4) the

assessment of ADHD requires evidence directly obtained from the classroom teacher

(or other school professional) regarding the core symptoms of ADHD, duration of

symptoms, degree of functional impairment, and associated conditions; 5) evaluation

of the child with ADHD should include assessment for associated (coexisting)

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conditions; and 6) other diagnostic tests are not routinely indicated to establish the

diagnosis of ADHD but may be used for the assessment of other coexisting

conditions (e.g., learning disabilities and mental retardation) (Homer et al.,

2000:1158).

When impairment criteria based on the Diagnostic and Statistical Manual (DSM) are

used, prevalence rates for ADHD in the general population of children and young adolescents

ages 6 to 12 range from 4 to 12 percent, with higher rates for urban areas, boys and those

from low SES backgrounds (Barbaresi, Katusic, Colligan, Pankratz, Weaver, Weber,

Mrazek, and Jacobsen, 2002; Brown et al., 2001; Pineda et al., 1999; Wender, 2002).

The etiology of ADHD is likely multifaceted, combining genetic, environmental, and

other risk factors. Research suggests, however, that approximately 80 percent of the variance

in ADHD is genetic; thus, the disorder travels along biological but not adoptive family lines

(Sprich, Biederman, Crawford, Mundy, and Faraone, 2000; Thapar, Harrington, Ross, and

McGuffin, 2000; Weiss, Hechtman, and Weiss, 1999; see also, Rowe, 2002). Other potential

causes of ADHD include adversity, obstetric complications, and exposure to nicotine and

alcohol as a fetus. Research has also shown that events such as car accidents that affect brain

development may cause ADHD. None of the studies we examined suggested that ineffective

parenting causes the onset of ADHD.

Children with ADHD frequently manifest a range of comorbid conditions including

aggression, conduct disorders, oppositional defiant disorder, poor academic and cognitive

skills, anxiety, depression, poor neuropsychological deficits, and low self-esteem (Barkley,

1998; Brown et al., 2001; Loeber and Loeber, 1998; Nadder, Silberg, Rutter, Maes, and

Eaves, 2001). Research suggests that individuals diagnosed with ADHD have higher rates of

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offending (Pratt, Cullen, Blevins, Daigle, Wright, and Unnever, Forthcoming; Satterfield,

Swanson, Schell, and Lee, 1994; Satterfield and Schell, 1997; Stern, 2001). It has been

estimated that more than 25 percent of adult prison inmates have ADHD (Foley, Carlton, and

Howell, 1996) and that in excess of 50 percent “exhibit a significant number of ADHD

symptoms” (Richardson, 2000:18-2). Most noteworthy, a recent meta-analysis revealed that

ADHD is associated with increased conduct disorders, drug use, and criminal behavior (Pratt

et al., 2001; see also, Hawkins, Herrenkohl, Farrington, Brewer, Catalano, and Harachi,

1998). Although research by criminologists on ADHD is not plentiful, findings from major

longitudinal studies show an association between various measures of ADHD and

delinquency; the strength of the relationship, however, appears to decline with age (Moffitt,

1990; Farrington, Loeber, and Van Kammen, 1990; Loeber, Stouthamer-Loeber, Van

Kammen, and Farrington 1991; Loeber et al., 2001). Relevant to our concerns here, none of

these studies explored systematically the relationships among ADHD, Gottfredson and

Hirschi’s construct of self-control, and crime.

Our special interest is in exploring the potential linkages between ADHD and the

general theory. In particular, we assess whether ADHD may be an independent source of

low self-control. If so, then this relationship would expand the general theory to consider the

possibility that self-control is not exclusively caused by parental management; it may also

have a genetic/biological component—a possibility that, as we have noted, Gottfredson and

Hirschi have downplayed or, in the least, have not taken the opportunity to systematically

develop. We also are interested in whether ADHD has direct effects on delinquency or

effects that are indirect through low self-control. If so, then this latter finding would advance

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research on ADHD by identifying a key factor through which ADHD increases delinquent

involvement.

RESEARCH STRATEGY

The current paper attempts to contribute to the empirical assessment of Gottfredson

and Hirschi’s theory in three ways. First, we provide another test of whether Grasmick et

al.’s (1993) measure of self-control is related to delinquency—assessed by self-reported acts

and by self-report arrests—among a sample of middle school students. Again, previous

research has shown this scale to be consistently related to delinquency (Pratt and Cullen,

2000). We should note that Hirschi and Gottfredson (1993:48-49) recommend measuring

self-control with behavioral measures (see also, Evans, Cullen, Burton, Dunaway, and

Benson, 1997). These were not available in the data set. However, existing studies have

shown self-control similarly affects crime whether it is measured attitudinally (as do

Grasmick et al.) or behaviorally (see Pratt and Cullen, 2000).

Second, we explore the relationships among parental management, self-control, and

delinquency. Gottfredson and Hirschi’s theory predicts that parental management should

directly affect self-control, which, in turn, affects delinquency. The general theory also

predicts that self-control should completely mediate the effect of parental management on

delinquent involvement.

As noted in the previous section, the extant research is largely supportive of the

general theory’s theses on the impact of parenting on self-control and on self-control’s role in

mediating parental effects on delinquency (Hay, 2001; Gibbs et al., 1998; Feldman and

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Weinberger, 1994). This research, however, has been limited by the use of small samples (n

= 87 to 267), samples of limited diversity (e.g., boys only, college students), and/or samples

composed of “older” youths (students in high school or beyond). In contrast, the sample in

the current study is large (over 2,400 students), racially and economically diverse, and

confined to middle school students. Accordingly, it presents the opportunity to assess the

generality of Gottfredson and Hirschi’s core parenting thesis among a large, socially diverse,

and youthful sample.

Indeed, we employ a sample that is considerably younger than those used in the most

systematic existing studies of Gottfredson and Hirschi’s parental management theses (Hay,

2001; Gibbs et al., 1998). Even so, because Gottfredson and Hirschi argue that self-control is

an enduring propensity by the end of childhood (around age 8), it might have been preferable

to have used a sample of grade school children. Gottfredson and Hirschi’s perspective,

however, largely emphasizes continuity in self-control. Given the short time span between

age 8 and the age of our sample (mean age 12), we would expect that the respondents’ self-

control—as well as their parents’ willingness to monitor the children’s behavior—would

remain relatively constant into the middle school years.1

Third, we explore the relationship between ADHD and delinquency. The extant

research has established an ADHD-crime connection (Babinski, Hartsough, and Lambert,

1999; Burke, Loeber, and Lahey, 2001; Pratt et al., Forthcoming). However, the general

theory would predict that any relationship between ADHD and delinquency should be

spurious. That is, the personality orientations associated with ADHD should not influence

delinquency beyond the effects of the theory’s concept of low self-control. Further, in a

causal model, ADHD—as a largely genetic/biological condition—can be placed prior to self-

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control; that is, it should be considered as an exogenous factor that potentially influences the

endogenous factor of self-control (Rowe and Osgood, 1984).

The potential criminogenic symptoms of ADHD, hyperactivity and impulsivity, are

similar to a core symptom of low self-control, impulsivity (Gottfredson and Hirschi, 1990).

Therefore, we hypothesize that students with ADHD should exhibit lower levels of self-

control, which, in turn places them at greater risk for offending. It is noteworthy, however,

that Gottfredson and Hirschi would predict that in such a model, ADHD should not

significantly impact levels of self-control. Instead, the general theory contends that the level

of self-control is determined principally by the quality of parenting management.

In summary, the causal relationships that we test are theoretically derived and

consistent with prior tests of the general theory’s parental management thesis (Feldman and

Weinberger, 1994; Gibbs et al., 1998; Hay, 2001). However, we recognize that alternative

causal relationships could be hypothesized (e.g., consistent with a “child effects” thesis, that

low self-control prompts poor parental management). Because our data are cross-sectional,

we are restricted in our ability to assess these possibilities. Accordingly, although an

advance in several ways over the extant research, future analyses may need to further specify

this present test of Gottfredson and Hirschi’s theory. To the extent our results confirm the

general theory, it gains credibility; in contrast, anomalous findings, would weaken the

perspective’s claim to be the general theory of crime (more generally, see Braithwaite, 1968).

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METHODS

Sample

The data for this study were collected in an effort to gather baseline data on school

bullying and school violence. The sample was drawn from the six public middle schools that

serve a metropolitan area with a diverse population of nearly 100,000 inhabitants in Virginia.

The six middle schools served a total enrollment of 3,038 students in grades six, seven, and

eight. Approximately, 46.5 percent of the middle school student population was nonwhite,

52 percent received a free or reduced cost breakfast or lunch at school, and 50 percent were

male. The percentage of students receiving some services in special education based on an

IEP (individualized education plan) was 19.6 percent and the dropout rate for the middle

schools in 1999-2000 was 1.9 percent.

All middle school students in attendance on the day of the survey were eligible for the

study.2 In all, 2,472 students completed the survey (a response rate of 81 percent). School

administrators sent an “opt-out” letter to all the parents/guardians of the students before the

administration of the survey. The parents of 42 students declined to allow their children to

participate in the survey. Teachers administered an anonymous survey in classrooms during

the fall of 2000. The 12-page op-scan survey consisted of 146 questions; including measures of

delinquency, parenting effectiveness, low self-control, ADHD status, and personal

characteristics of the students.

The respondents who completed the survey closely matched the total population of

students. The percentage of students who reported they were non-white was 40 percent in

comparison to the student population of 46.5 percent; the percentage of male study

participants was 49.1 percent in comparison to the student population of 50 percent; and the

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percentage of students who reported that they received a free or reduced cost breakfast or

lunch was 50 percent in comparison to the student population, for grades 7-12, of 52 percent.

Surveys were carefully screened for complete and accurate information (patterned

responses). We deleted 31 surveys in which the students gave the same response to every

question on one or more pages (excluding the pages focused on bullying and having been

bullied). We also dropped four surveys in which the student reported an unlikely height (over

six foot five inches) or weight (over 300 pounds). We confirmed with school principals that no

students in the school were this large.

Using LISREL 8.50 for Windows and the EM algorithm (Schafer, 1997), we substituted

values for missing cases. The EM algorithm generated values based on a data set that included

the variables used in the present analysis. All analyses were run with and without missing

cases; the results did not differ substantively.3 After imputing values for the missing cases, the

sample included 2,437 middle school students. Ordinary least squares was used as the

estimation procedure when we analyzed the low self-control and delinquency scales and logistic

regression was used when we analyzed the dichotomous dependent variable, self-reported

arrest.

Measures

Table 1 shows the coding of the variables included in this analysis. Variable names are

in the first column, the coding categories are in the second, and descriptive statistics are in the

last columns.

-----Insert Table 1 About Here-----

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Dependent Variables. A self-report instrument adapted from the National Youth

Survey was used to measure delinquent involvement (Elliott, Huizinga, and Ageton, 1985;

Elliott, Huizinga, and Menard, 1989). To ensure a clear reference period for offending,

students were asked to indicate how often “since school started in August” that they had

engaged in nine relatively serious delinquent acts. The survey was administered in the last

week in October (in 2000). To respond to the self-report items, the youths used a scale

ranging from “0 = never” to “4 = 4 or more times.” The acts on the measure of delinquent

involvement included purposely damaging property belonging to a school, purposely setting

fire to a building/car or other property, carrying a hidden weapon, attacking someone with

the idea of seriously hurting or killing them, and getting involved in gang fights. They also

included hitting or threatening to hit a teacher or other adult at school, selling illegal drugs,

hitting or threatening to hit a parent, and using force or threatening to use force to get money

or things from other people. The students’ scores were summed across these nine items to

create the Delinquent Involvement scale. Its alpha coefficient was .90. Thirty-four percent of

the 2,437 middle school students reported that they had committed at least one delinquent

act. The log transformation of the scale scores was used in the analysis given that it was

positively skewed.

In addition to the delinquency scale, we measured self-reported arrest. The

respondents were asked if they had ever “been arrested by the police for anything other than

a minor traffic offense.” Six percent of the 2,437 students reported that they had been

arrested. We used an “ever” measure because with a sample of youths with a mean age of 12

(as in our sample), a short reference period (“since school started”) would have yielded too

few arrests to permit a meaningful analysis. The major purpose of including this measure

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was to examine whether the results on the self-report scale—our main measure of

offending—would be robust across a measure of “official” delinquency. Thus, although the

measure of arrest must be viewed with caution, it provides an opportunity to explore the

consistency of the findings across different means of assessing delinquency.

Independent Variables. The Grasmick et al. (1993) scale was used to measure the

respondent’s level of low self-control. Responses were summed across 22 items and the scores

were standardized.4 The alpha coefficient for Low Self-control was .89. This reliability is

consistent with previous research (Arneklev, Grasmick, Tittle, and Bursik, 1993; Gibbs and

Giever, 1995; Gibbs et al., 1998; Hay, 2001; Longshore, 1998; Longshore and Turner, 1998;

Piquero and Tibbetts, 1996). Note that we have coded this measure so that a high score means

that a respondent has “more” low self-control (i.e., the respondent has a higher “criminal

propensity”).

In Gottfredson and Hirschi’s perspective, effective parental management hinges on

parents monitoring their children, recognizing deviant behavior when it occurs, and then

consistently punishing misconduct. We measure two of these dimensions, monitoring and

consistent punishment. No measure was available for how parents differed in their recognition

of wrongdoing. Past research has also omitted this dimension of parental management (e.g.,

Hay, 2001). Hay (2001:715) contends that omitting this dimension of parental management

may be “acceptable” because “research has consistently identified monitoring and discipline as

the key aspects of effective parenting.”

Parental monitoring was measured through a scale developed by Simons, Wu, Conger,

and Lorenz (1994). This scale assessed how effectively parents or guardians monitored the

behavior of their children. The responses ranged from never (never = 0) to always (always = 4).

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Responses were summed across five items (e.g., How often do/does your parent or parents

(guardians) know who you are with when you are away from home?) and the scores were

standardized. The alpha coefficient for the Monitoring scale was .75, which is similar to the

alpha reported by Simons et al.

A single item created by Simons et al. (1994) was used to measure consistency of

discipline: “How often do/does your parent or parents (guardians) punish you for something at

one time and then at other times not punish you for the same thing?” The responses ranged

from never (never = 0) to always (always = 4). This variable was recoded so that a high score

meant more consistency in punishment. The variable was termed consistent punishment.5

The data set did not contain a standard psychometric measure of ADHD. We also were

precluded from consulting school records for official psychological diagnoses, because the

promise to parents of complete anonymity on the survey precluded this option (i.e., no

identifiers on the survey instrument). The data set, however, did contain an alternative means of

assessing ADHD status. Specifically, we used a question in the survey instrument that asked

students: "Have you ever taken medication for being hyperactive (attention-deficit hyperactivity

disorder)?"6 Notably, epidemiological researchers have also used surveys including questions

about ADHD medication status to measure its prevalence (Rowland, Umbach, Stallone, Naftel,

Bohlig, and Sandler, 2002).

We believe that our measure is a reasonable proxy for ADHD for the following

reasons. First, to receive a prescription for ADHD medication, students must have been

referred to a physician and found to have symptoms consistent with ADHD. It is standard

practice in the schools surveyed for physicians to prescribe medication only after consulting

with teachers and parents about the child’s behavior. It also is relevant that the use of a

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middle school sample likely means that clear cases of ADHD would have been diagnosed by

the age at which the students completed the survey (i.e., a younger sample would have had

less “time” to be diagnosed with ADHD).

Second, the ADHD prevalence rates using the medication status measure are consistent

with the extant research. In the current study, 14 percent of the 2,437 students (n = 359)

reported having ever taken medication for ADHD. This percentage is similar to the rate of

ADHD found in low income community samples in which DSM impairment criteria are used to

establish prevalence rates (Brown et al., 2001; Pineda et al., 1999). In addition, the majority of

the students who reported ever having taken medication for ADHD were boys (69 percent, n =

249). This finding is consistent with previous research showing that boys have higher

prevalence rates of ADHD (Pineda et al., 1999).

Third, the results we present on the relationships of the medication status measure of

ADHD to self-control and to delinquency are in the predicted direction. Measurement error

might influence the strength of the statistical associations we report. However, the fact that the

ADHD measure is “behaving” in a theoretically anticipated, rather than counterintuitive, fashion

suggests that it is assessing the underlying construct in a reasonable way. Additionally, it is

instructive that the measure we employ also has findings consistent with those based on research

using alternative measures of ADHD (Pratt et al., Forthcoming; Satterfield, Swanson, Schell,

and Lee, 1994; Satterfield and Schell, 1997; Stern, 2001).

Fourth, it could be argued that our measure of ADHD—medication status—reflects

external efforts at “social control” rather than a child’s behavior. With regard to the current

study, the most salient control thesis would be that medication status reflects not ADHD but

parental management (cf. Moffitt, 1990). Two possibilities exist: (1) children receive

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medication because their parents are ineffective managers and use it to control their children, or

(2) children receive medication because their parents are effective managers and monitor them

closely enough to know when intervention is needed. To some degree, the control of parental

management in the multivariate analysis would account statistically for any spurious

relationship of ADHD medication status to delinquency that is due to parental behavior. More

directly, however, we explored in our data the zero-order correlations between the parenting

variables and ADHD medication status. The relationships were substantively weak, with

ADHD having a -.04 correlation with parental monitoring and a .07 relationship with consistent

punishment. These finding are not surprising given that previous research suggests that ADHD

has a large genetic component and is not a product of parenting (Rowe, 2001).

Again, the use of a limited measure of ADHD means that the results should be viewed

with appropriate caution. Using medication status as a proxy for a clinically defined case of

ADHD introduces some measurement error into our analysis. Studies have shown that whites

are more likely to receive medication for ADHD than blacks (Rowland et al., 2002; Safer and

Malever, 2000). It is also possible that we have children in the sample who report that they

have taken medication for ADHD but do not have a clinical case of ADHD. Research shows

that there has been a several-fold increase in prescriptions for stimulant medication among

children during the past decade (Safer, Zito, and Fine, 1996). In addition, there are significant

regional variations in the amounts of stimulants prescribed by physicians and surveys among

primary care pediatricians and family physicians reveal wide variations in practice patterns

about diagnostic criteria and methods (Homer et al., 2000). However, it is also noteworthy that

recent research indicates that only .2% of subjects in a population-based birth cohort study with

no evidence of ADHD were treated with a stimulant medication (Barbaresi et al., 2002).

21

It is more likely that our self-report measure based on medication status underestimates

rather than overestimates the prevalence of ADHD in the population of middle school students

we studied. Barbaresi et al. (2002) found that only 72.1% of the subjects in the definitive

ADHD group were treated with stimulants alone or in combination with either tricyclic

antidepressants or centrally acting α-adrenergic agonists, 14.4%. Nonetheless, for the reasons

stated, it seems unlikely that the findings we report can be attributed to a methodological artifact

inherent in the measure of ADHD. At the very least, the medication status measure allows us to

conduct analyses that result in provisional specifications of Gottfredson and Hirschi’s theory

that future researchers will need to take into account.

Control Variables. The control variables are listed in Table 1. They included student

grade level (grades six, seven, and eight) and gender. As a rough index of socioeconomic

status, we identified students who reported receiving a free or reduced cost meal at school. An

income chart adjusted by household size determines whether a student qualifies for a free or

reduced cost meal. For example, a student living in a household of four whose annual income

does not exceed $22,945 can qualify for a free meal and can qualify for a reduced cost meal if

the annual income does not exceed $32,653. In addition, children can qualify for a free meal if

they are a foster-child, live in a household receiving food stamps, or if they get TANF

(Temporary Assistance for Needy Families). Of the 2,437 students, 49 percent reported

receiving a free or reduced cost breakfast or lunch at school. For race, we included a

dichotomous variable to identify African-American students, the only minority group large

enough for statistical analysis (n = 978, 49 percent of the 2,437 students).

22

RESULTS

It is important to initially establish whether middle school students with ADHD are

more likely to report that they have engaged in delinquency. Collapsing the delinquent

involvement scale into a dichotomous variable and cross-tabulating it with ADHD shows that

41 percent of the students who reported ever having taken medication for ADHD reported

committing at least one delinquent act. In comparison, 33 percent of the non-ADHD students

committed at least one delinquent act, a difference of 8 percent. The student’s ADHD status

is significantly associated with the logged delinquent involvement scale (Chi-square =

359.43, d.f. = 277, p = .0006).

-----Insert Table 2 About Here----

If Gottfredson and Hirschi (1990) are correct, parental monitoring and consistency of

punishment—not ADHD—should be the chief predictors of level of self-control. Model I in

Table 2 shows the results of regressing the Grasmick et al. (1993) scale on ADHD status, our

measures of parental management, and the other characteristics of the respondents. In

support of the general theory, children who report that they were effectively parented are

likely to report having more self-control (or, conversely, those who were ineffectively

monitored and inconsistently punished had higher levels of low self-control). In addition, the

beta weights indicate that the parental monitoring and consistent punishment measures have

the greatest affect on low self-control. These findings affirm Gottfredson and Hirschi’s

(1990) assumption that ineffective parenting is a major correlate of low self-control.

23

However, the results from Model I do not fully conform to Gottfredson and Hirschi’s

(1990) predictions. Most noteworthy, the regression equation shows that ADHD status is

significantly related to low self-control while holding constant the effects of the other

variables in the equation. This means that the effect of ADHD status on low self-control is

independent of the effects of parental management and the student’s other sociodemographic

characteristics. In addition, the regression equation indicates that boys, African-Americans,

and those who received a free or reduced cost meal are more likely to report having low self-

control.

Our next concern is whether youths with low self-control are more likely to offend

after holding constant the effects of the other variables in the regression equation and

whether ADHD and the parental management measures directly affect offending. If

Gottfredson and Hirschi (1990) are correct, the only reason why students should have higher

rates of offending is because they have less self-control. That is, the respondents’ ADHD

status, whether they were ineffectively parented, and their other sociodemographic

characteristics should not be associated with their offending. We created two models,

Models II and III, to assess these possibilities. Model II investigates whether ADHD status

and the parental management measures are associated with delinquent involvement without

low self-control included in the equation. Model III examines whether low self-control

mediates the potential influence of ADHD status and the parental management measures on

delinquent involvement and whether low self-control directly affects the delinquency scale.

Model II indicates that, without low self-control in the regression equation, there is a

significant relationship between ADHD status and delinquent involvement. Respondents

with ADHD are more likely to report that they committed delinquent acts. In addition,

24

youths who report that they were effectively parented report lower rates of delinquency. This

is particularly true for respondents who report that their parents closely monitored them. In

addition, boys, African-Americans, those who receive a free or reduced cost meal, and those

in upper-grade levels report more delinquency.

Model III confirms Gottfredson and Hirschi’s (1990) prediction that youths with low

self-control are more likely to engage in delinquency. However, our results do not support

their prediction that low self-control would be the only variable associated with higher

delinquency. Boys, African-Americans, and those in upper-grade levels report higher

delinquent involvement. It is instructive that even with low self-control in the model,

parental monitoring retained an independent effect on delinquency, with the presence of

monitoring leading to lower involvement in delinquent conduct. Consistency of parental

punishment, however, had no direct effect on the delinquency scale.

Of additional interest, respondents who report having ADHD report higher levels of

delinquency, although the association was statistically and substantively marginal (p =.059, β

=.03). Low self-control mediated half of the effect of ADHD on delinquency.7

Figure 1 presents a path diagram illuminating the relationships among ADHD status,

parenting, low self-control, and delinquency. Because the data are cross-sectional, this

model should be seen as suggestive and viewed with appropriate caution. The causal model,

however, is consistent with the logic of the general theory. ADHD is placed as a precursor to

self-control because of its likely genetic/biological origins.

As expected, and as predicted by Gottfredson and Hirschi (1990), the path model

shows that low self-control is a strong predictor of delinquent involvement. Also in line with

the general theory, parental management variables were related to self-control, which

25

partially mediated their effects on delinquency. Contrary to the general theory, however,

parental monitoring exerted a strong independent effect on delinquent involvement that was

not mediated by low self-control. Thus, it appears that youths closely monitored by their

parents have low levels of delinquency or, conversely, that ineffective parenting directly

increases delinquent involvement. The path diagram also shows that gender, race, and year

in school directly affected delinquency, but the relationships were weak.

The direct impact of ADHD on delinquency was substantively and statistically

marginal. More relevant, however, is that Figure 1 shows that ADHD is a source of low self-

control. Again, the general theory would not predict this finding.

-----Insert Figure 1 About Here-----

In addition to the measure of self-reported delinquency, we reanalyzed the data using

a dichotomous measure of self-reported arrest (i.e., “arrested for something other than a

minor traffic offense”). Model IV in Table 2 presents the logistic regression results from our

analysis of the arrest data. The results from this analysis are nearly identical to those in

Model III, which are based on self-reported delinquency. Both models show that the

respondents most at risk for offending have low self-control, have ADHD, are boys, and

report having been poorly supervised. There are three differences between the models.

African-Americans are more likely to report that they have engaged in delinquent acts, but

we found no race differences in the arrest data. In addition, socieconomically disadvantaged

middle school students are more likely to have been arrested but do not report engaging in

more delinquency. We also found that students in upper grade levels report they have

committed more acts that are delinquent but are not more likely to be arrested.

26

DISCUSSION

Since its publication in 1990, Gottfredson and Hirschi’s “general theory” has exerted

an enormous influence on criminological theory and research. The theory challenged many

beliefs in the field—among them that social experiences beyond childhood matter (cf.

Sampson and Laub, 1993)—and prompted a wealth of research on its clearly stated

propositions (for a summary, see Pratt and Cullen, 2000). The present study has attempted to

contribute to the research on the general theory by assessing key aspects of this approach that

heretofore have not been thoroughly investigated. The findings support the general theory

but also reveal that important aspects may be overstated and in need of qualification.

First, we provide data on the Gottfredson and Hirschi’s core proposition that low self-

control is positively related to delinquent involvement. This relationship appears to be both

strong and consistent. Thus, the low self-control-delinquency relationship holds across both

self-reported delinquency and self-reported arrests.

It is also noteworthy that these findings are consistent with a growing body of

research in which the Grasmick et al.’s (1993) measure of self-control has been found to be

related to delinquent involvement (Pratt and Cullen, 2000). The Grasmick et al. scale is

important because it has high face validity. Many other measures of self-control used to test

the general theory were constructed from items within secondary data sets (see, e.g.,

Avakame, 1998; Brownfield and Sorenson, 1993; Paternoster and Brame, 1998; Polakowski,

1994). In contrast, Grasmick et al. developed their scale to measure specifically the main

features of low self-control that Gottfredson and Hirschi attributed to the concept. That is,

their operationalization of low self-control was theoretically derived. This close connection

27

between theory and method lends confidence that Gottfredson and Hirschi have identified a

factor that must be seen as an important predictor of criminal conduct.

The more problematic aspect of Gottfredson and Hirschi’s theory is the contention

that low self-control is the only criminal propensity. In advancing a “general” theory,

Gottfredson and Hirschi were not identifying multiple propensities or risk factors. Instead,

they used the concept of self-control to describe the very nature of criminal propensity. This

is a bold assertion that almost certainly is inaccurate. Existing research on criminogenic risk

factors supports the idea that low self-control fosters misconduct—including recidivism—but

self-control is but one of several factors consistently and strongly related to criminal

involvement (Andrews and Bonta, 1998; Hawkins et al., 1998; Lipsey and Derzon, 1998). In

particular, there is evidence that social learning variables (e.g., antisocial values, antisocial

associations) are also predictive of wayward behavior (Andrews and Bonta, 1998; Evans et

al., 1997; Pratt and Cullen, 2000; Sellers, Pratt, Winfree, and Cullen, 2000).8 Still, it is

salient that self-control, as identified by Gottfredson and Hirschi, is a factor that should be

incorporated into our understanding of crime causation. Its relationship to delinquent

involvement is a “fact” for which extant theories must account.

Second, beyond assessing the self-control and delinquency relationship, our study

focused on a theoretical proposition that has received limited empirical investigation:

Gottfredson and Hirschi’s contention that parental management (or “direct control”) is the

main source of low self-control and, by implication, has effects on delinquency only through

low self-control. Our study suggests that the origins of self-control are likely to be

multifaceted and not limited to parental management. Importantly, we found that ADHD

medication status was a source of low self-control—a point that we will return to below.

28

Our analysis revealed partial support for Gottfredson and Hirschi’s view on parental

management, self-control, and delinquency. Parental monitoring and consistent punishment

were clearly related to self-control in the predicted direction, with higher levels of monitoring

and consistency in punishment associated with higher levels of self-control. Contrary to

Gottfredson and Hirschi’s perspective, however, parental monitoring has substantial effects

on both self-reported delinquency and arrests independent of low self-control. This suggests

that Gottfredson and Hirschi’s conceptualization of “why parents matter” is excessively

narrow. Parental monitoring may have direct effects or perhaps “work through” other

processes. For example, social learning theory is clear in suggesting that effective parental

monitoring would result in lower delinquent involvement (Patterson, Debaryshe, and

Ramsey, 1989).

Another possibility is that parental monitoring limits offending not only by increasing

self-control but also by reducing criminal opportunities. LaGrange and Silverman (1999:47),

for example, suggest that the “degree to which parents monitor where teens are and who they

are with can be expected to have a direct impact on their opportunities to offend.” If so, this

could explain why parental management not only indirectly effects delinquency through self-

control but also has a direct effect.

We should note, however, that Gottfredson and Hirschi do not explore this

possibility, but instead link ineffective parenting only to the development of criminal

propensity or, in their words, low self-control. They recognize the role of opportunity in

making crime possible, but they largely see opportunity as involving either situational

contingencies (e.g., one must be employed to embezzle) or the basic elements of the crime

(e.g., one has to have alcohol and a vehicle to engage in drunk driving) (Gottfredson and

29

Hirschi, 1990:190; Hirschi and Gottfredson, 2001:88). Additionally, Gottfredson and Hirschi

argue that because crime is “easy” to commit, criminal opportunities are ubiquitous. Given

these considerations, it is not clear whether they would theorize that parental monitoring is an

effective direct control that reduces offending through opportunity reduction. First, because

parents are not physically present in many situations where criminal opportunities abound,

their ability to significantly deter crime is likely to be minimal. Second, the clear emphasis

of Gottfredson and Hirschi’s model is that parental monitoring is a key ingredient in instilling

self-control that will operate to limit offending even when parents are absent and cannot

exercise direct control. Still, LaGrange and Silverman (1999) raise an interesting possibility

about the multiple effects of parental monitoring that future research should explore.

Consistency of punishment was related to low self-control and, as the general theory

would predict, its effects on delinquency were mostly mediated by low self-control. Still, the

impact of consistency of punishment on self-control and delinquency was weaker than

parental monitoring. It is possible that the differential influence of these two variables is due

to our use of a single-item measure of what is a potentially complex social process. Another

possibility is that monitoring is more consequential than consistency in parents’ influence on

misconduct. We propose this possibility only tentatively, but it does suggest that future

research should focus on distinct components of parental management and not assume that

all control strategies used by parents are equally consequential (Hay, 2001).

Our results might also be questioned because we use a middle school sample to assess

Gottfredson and Hirschi’s propositions on parental management. Gottfredson and Hirschi

assert that parenting in childhood establishes enduring individual differences in self-control

(or criminal propensity). Ideally, one might wish to test their perspective on a sample of

30

children under the age of eight. Even so, the sample used in our study is one of the youngest

samples used to test the general theory (the average age of our sample is 12) (Pratt and

Cullen, 2000). It also is much younger than the college student and high school samples used

by the most systematic previous studies (Gibbs et al., 1998; Hay, 2001). Furthermore, as

noted previously, the logic of the general theory would suggest a stable relationship between

parenting and self-control during childhood and into early adolescence. Gottfredson and

Hirschi’s perspective downplays change in propensity and human conduct in favor of

stability in relative individual differences in self-control across time and situations (Hirschi

and Gottfredson, 1995; Sampson and Laub, 1995). Minimally, it is likely the general theory

would predict that child-parent relations at age eight would be similar to those that exist as

the children move into early adolescence.

Finally, the current study attempted to advance the general theory by interjecting

ADHD into the analysis. Although exceptions exist (Farrington, Loeber, and Van Kammen,

1990; Moffitt, 1990; Loeber et al., 1991), research on ADHD and youth problem behavior

has been conducted primarily by psychologists and biomedical researchers with few ties to

the field of criminology. This omission is potentially problematic in that ADHD

characterizes a meaningful subset of young adolescents and is a risk factor for a range of

negative life outcomes. In our analysis, the inclusion of ADHD status allowed for two

interrelated insights that were theoretically consequential.

First, for the general theory, the relationship of ADHD to low self-control suggests

that the origins of self-control are not limited to parental practices. Because ADHD almost

certainly has genetic/organic causes, this suggests that low self-control is not a purely social

outcome but also is affected by genetic predispositions (e.g., related to temperament). It is

31

noteworthy that, although Gottfredson and Hirschi (1990:96) raise the possibility that self-

control might be influenced by “individual differences,” 9 they refrain from developing this

insight or incorporating such factors systematically into their paradigm (see also, Hirschi and

Gottfredson, 2001).10

Second, for ADHD researchers, it appears that their models—by ignoring

criminological theory and studies—are almost certainly misspecified or, in the least,

incomplete. It is important that we found that ADHD status’ effect on delinquent

involvement was almost completely indirect through low self-control. Apparently, ADHD is

a condition that does not consign youths to delinquent behavior. Rather, it likely exposes

them to risk factors—such as low self-control—that may foster delinquency in the absence of

appropriate intervention.

The findings we report in this paper on ADHD, self-control, and delinquency are

fairly robust. They hold across both the measure of self-reported delinquency and across the

measure of self-reported arrest. The sample used also had the advantage of being diverse

socially and economically, thus increasing the likely generalizability of the findings. Even

so, future research would benefit from assessing these relationships for different age groups

and across different social contexts. We also caution the reader that we may have

underestimated the effects of ADHD. Recent research indicates that the prevalence of

treatment with stimulant medication was 86.5% for definite ADHD and 40% for probable

ADHD (Barbaresi et al., 2002). Our sample is limited to students who report having received

medication for ADHD. Thus, future research may want to replicate our findings using

multiple measures of ADHD, including independent psychological diagnoses (where

feasible) and/or multiple-item measures of the construct. Regardless, the current study does

32

help to lay a foundation for research on ADHD and delinquency in general and for research

seeking to extend Gottfredson and Hirschi’s insights into the relationships among parenting,

self-control, and crime.

33

FOOTNOTES

1. Gottfredson and Hirschi argue that although absolute levels in criminal propensities

may change over the life course, people’s individual differences in self-control established in

childhood will remain stable relative to one another across time. In any event, because we

employ a young sample (average age of 12), changes in levels of self-control should not

influence the results. We also note that Gottfredson and Hirschi indicate that their theory

should be tested with cross-sectional data, suggesting that relationships between key

variables persist across the life course.

2. An alternative school, whose enrollment includes fifty seventh and eighth graders,

ten of whom are in a separate self-contained program at another site, was excluded from

participation in the survey.

3. For example, when we ran Model I in Table 2 with and without missing cases, the

coefficient for ADHD differed by .02 (-.12 versus -.10), the coefficient for consistency in

punishment differed by .04 (-.21 versus -.17), and for the monitoring scale, the difference

was .07 (.37 versus .30). For Model III, the coefficient for ADHD remained the same at .03;

the coefficient for consistency in punishment differed by .02 (-.02 versus .-04 ); and for the

monitoring scale, the difference was .01 (-.26 versus -.27). The self-control standardized

regression coefficient differed by .05 (.26 versus .21).

4. Grasmick et al.’s (1993:17) scale initially contained 24 items, but they deleted one

item related to physical activities because it lowered the scale’s reliability. Accordingly, the

original 23 questions from the Grasmick et al. scale were included on the current survey.

However, two questions were placed on the same line of the survey, thus inadvertently

34

combining them into one question. The two questions were listed as “The things in life that

are easiest to do bring me the most pleasure, I dislike really hard tasks that stretch my

abilities to the limit.” Consequently, students responded to 22 instead of 23 items. This

slight departure from the original scale had no discernible effect on the scale’s reliability,

which, as noted, remained high (.89).

5. The data set did not contain a measure of opportunity, an omission that

characterizes other studies on parenting, self-control, and delinquency. It is difficult to know

how the omission of opportunity would affect the results, because Gottfredson and Hirschi do

not develop a theory of opportunity in which they specify how the relationship of self-control

to offending will be conditioned by this factor over time and across a sample of youths.

Gottfredson and Hirschi do say that opportunity is a necessary condition for a criminal event

to occur. On the other hand, they also suggest that crime is easy and requires little planning,

suggesting that criminal opportunities are ubiquitous. If so, then opportunity would not be

expected, across time, to have a large impact on the relationship between self-control and

offending (i.e., if opportunities are prevalent, criminal propensities would have ample

chances to flourish across time). As we note later, however, it is possible that parental

management could not only influence self-control but also limit opportunities to offend. The

implications of this insight are explored in the discussion section.

6. The respondents were not asked if they were currently on medication and, if so, what

type and dose of medication. In retrospect, this may have been useful information to include.

Even so, the current study is interested in how ADHD is related to delinquency across a sample

of middle school students. In any population, the ADHD cases will include respondents

currently taking and not taking medication. Notably, to our knowledge, no previous

35

criminological study examining ADHD has controlled for whether respondents were currently

taking prescribed medication (Moffitt, 1990; Farrington, Loeber, and Van Kammen, 1990;

Loeber, Stouthamer-Loeber, Van Kammen, and Farrington, 1991; Loeber et al., 2001; Pratt

et al., 2001).

7. Because the research on ADHD has found that boys are more likely to have ADHD

and comorbid conditions (e.g., conduct disorders) (Gaub and Carlson, 1997; Weiss,

Hechtman, and Weiss, 1999), we tested for the interaction term, Gender X ADHD in the

main effect equation presented in Model III. It was insignificant. This suggests that the

relationship between ADHD and self-reported delinquency does not vary across gender.

Further, for exploratory purposes, we also examined whether there was an interaction effect

between ADHD and self-control. The interaction term of ADHD X low self-control was

significant. Generating separate regression lines by substituting mean values showed that the

youths most at risk for offending were those with ADHD and low self-control.

8. It should be noted that the relationship of social learning variables to crime is

partially due to self-selection effects or, in other words, “birds of a feather flocking together.”

Even so, individual differences cannot account fully for social learning effects (Akers, 1998;

Warr, 2002). Further, in empirical tests of the general theory, measures of self-control do not

render social learning variables spurious (Evans et al., 1997; Pratt and Cullen, 2000).

9. We should note that it is possible to interpret Gottfredson and Hirschi’s statements

on individual differences in an alternative fashion. As a reviewer pointed out, Gottfredson

and Hirschi could be seen as arguing that genetic/biological traits do not lead “inexorably” to

crime. In this view, Gottfredson and Hirschi were raising the possibility that parenting

mediates the effects of genetics/biology on delinquency. This is a different position than

36

interpreting, as we do here, that Gottfredson and Hirschi largely discounted potential

genetic/biological causes of crime (whether they are direct or indirect). If this former

interpretation is correct, then our study is best seen more as a favorable, rather than as a

critical, elaboration of the general theory (since the effect of ADHD on delinquency is

substantially mediated by self-control).

10. Gottfredson and Hirschi (1990) might also wish to consider the impact of ADHD

and other individual differences (beyond low self-control) on the ability of parents to manage

their children effectively. The challenges for parents of ADHD children are potentially

greater because they are, themselves, at risk of having ADHD.

37

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Table 1. Coding of Variables Variable Names Coding/Range Mean Standard Deviation Gender

0=female 1=male

.48 .49

Received Federally Funded Meal

0=no free meal 1=free meal

.49 .50

Race

0=other 1=African American

.40 .49

Student Grade Level

0=sixth grade 1=seventh grade 2=eighth grade

.95 .81

Parental Monitoring

-4.65 - 1.02 Higher standardized scores indicating more closely monitored

-.05 1.02

Consistency of Parental Punishment

0=always 4=never Recoded so that higher scores indicate more consistent punishment

2.54 1.25

ADHD

0=not taken medication 1=has taken medication for ADHD

.14 .35

Low Self-control

-2.92 - 3.215 Higher standardized scores indicating less self-control

.07 .87

Involvement in Delinquency

0 - 3.49 Higher logged scores indicating more involvement in delinquency

.36 .68

Ever been Arrested 0=not arrested 1=arrested

.06 .24

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Table 2. Determinants of Low Self-Control, Delinquent Involvement, and Arrests Variables Model I

Low Self-Control

Model II Delinquent Involvement without Low Self-Control

Model III Delinquent Involvement with Low Self-Control and ADHD

Model IV Arrest with Low Self-Control and ADHD*

Gender (1=male)

.156*** (.273) [.029]

.092*** (.127) [.025])

.050** (.069) [.025])

.655*** 1.926 [.201]

Race (1=Black)

.066*** (.118) [.031]

.076*** (.107) [.027]

.058*** (.082) [.026]

-.062 .940 [.188]

Received Federally Funded Meal

.161*** (.281) [.031]

.053** (.073) [.027]

.010 (.013) [.026]

.832*** 2.299 [.213]

Grade Level .000 (.000) [.018])

.036* (.030) [.015]

.036* (.030) [.015]

.198 1.219 [.113]

Parental Monitoring

-.371*** (-.316 [.014])

-.362*** (-.242) [.012]

-.261*** (-.175) [.013]

-.168* .845 [.075]

Consistency of Parental Punishment

-.213*** (-.148) [.011]

-.079*** (-.043) [.010]

-.022 (-.012) [.010]

-.026 .974 [.069]

ADHD (1=ADHD)

.129*** (.318) [.041])

.069** (.134) [.036]

.034 (.066) [.035]

.474* 1.607 [.207]

Low Self-Control

----- ----- .269*** (.211) [.016]

.961*** 2.615 [.120]

R2 .330*** .191*** .242*** .213*** *Note: For Models I, II, and III: standardized estimates are listed first and have no parentheses or brackets; unstandardized estimates are listed below the standardized estimates and are in parentheses( ); standard errors are listed last and are in brackets [ ]. Model IV presents logistic regression coefficients, log odd ratios, and the standard errors are in brackets. The R2 for the Arrest equation is the Max-rescaled R2. * p <.05 ** p <.01 *** p <.001 N = 2437

45

Delinquent Involvement

Male African-American Free Meal Consistency in Punishment Parental Monitoring Grade Level ADHD

Low Self-Control

Figure 1. Path Diagram of Determinants of Low Self-Control and Delinquent Involvement

-.371

.156

.066.050

.058

.269

-.261

.034

.129

.161

-.213

.036